Deep neural networks (DNNs) receive great achievements in a variety of classification tasks, , image classification, object detection, semantic segmentation, and speech recognition [1]}, [2]}, [3]}, [4]}.
Deep learning, as a branch of machine learning, is to build DNN models from the training data, and deploy the well-trained model to the domain that their distributions are assumed the same as the distribution of training data.
However, data in real-world is not usually tailored to fit the assumption of identical distribution to the training data,
which makes the quality of predictive uncertainty of the DNN models unsure [5]}.
The predictive uncertainty of DNN models is closely related to the problem of model-specific out-of-distribution (OOD) that distinguishes abnormal samples far away from the distribution of training data [6]}.
The latter, , detecting OOD data for a given DNN model, is one of the essential requirements when the model prepares for deployment [7]}.
